Intelligent Database Control Systems with Automated Request Assessments

ABSTRACT

Aspects of the disclosure relate to intelligent database control systems for performing automated request assessments. In one embodiment, a computing device may receive, via a communication interface, a database request from a user computing device. The computing device may generate a legitimacy score associated with the database request based on one or more predetermined definitions. If the legitimacy score is above a predetermined threshold, the computing device may format the database request based on one or more of the predetermined definitions and the legitimacy score and command, via the communication interface, one or more databases to execute the database request. The computing device may format result set generated from the executed database request and transmit, via the communication interface, the formatted result set to the user computing device.

FIELD

Aspects of the disclosure relate to database control systems. Inparticular, one or more aspects of the disclosure relate to intelligentdatabase control systems for performing automated request assessments.

BACKGROUND

In database control systems, a database administrator may be tasked, insome instances, with receiving database requests to execute a series ofqueries from users without appropriate execution permissions. Thedatabase administrator may then be required to validate the authenticityof the users associated with the database requests, examine the databaserequests for definition statements (e.g., create, delete, drop, alter,and the like), analyze the data sensitivity result sets for which theusers are allowed access, examine the order of execution when multiplescripts are provided, analyze the query syntaxes for optimized usage ofdatabase resources, examine the request execution duration so that itdoes not affect the other requests in a queue, package the result setsto the users in the required format and communication mode, and thelike. Such processes may become complex to control when the databaseadministrator is required to cater the requests through a plurality ofschemas and databases. Further, by requiring the database administratorto perform the aforementioned tasks, the database control system may beprohibitive to the implementation of machine learning based automatedquery methods. Moreover, the ad hoc queries provided by the databaseadministrator in response to database requests from users withoutappropriate execution permissions may negatively impact the processingperformance and efficiency, memory allocation, and input/outputprotocols of the database control system.

SUMMARY

Aspects of the disclosure address these and/or other technologicalshortcomings by providing an intelligent database control system forperforming automated request assessments.

In particular, one or more aspects of the disclosure provide effective,efficient, scalable, and convenient technical solutions that address andovercome the technical problems associated with receiving databaserequests from users without appropriate execution permissions atdatabase control systems. For example, one or more aspects of thedisclosure provide techniques for performing automated requestassessments.

In accordance with one or more embodiments, a computing device having atleast one processor, communication interface, input mechanism, andmemory, may receive, via the communication interface, from a usercomputing device, a database request. The computing device may generatea legitimacy score associated with the database request based on one ormore predetermined definitions and may determine whether the generatedlegitimacy score is above a predetermined threshold. Responsive todetermining that the legitimacy score is above the predeterminedthreshold, the computing device may format the database request based onone or more of the predetermined definitions and the legitimacy score.The computing device may command or instruct, via the communicationinterface, one or more databases to execute the database request. Thecomputing device may format a result set generated from the executeddatabase request and may transmit, via the communication interface, tothe user computing device, the formatted result set.

In some embodiments, the legitimacy score may be generated by thecomputing device by way of machine learning algorithms such as linearregression, logistic regression, decision tree, support vector machine(SVM), Naïve Bayes, k-nearest neighbors (KNM), k-means, random forest,dimensionality reduction, gradient boosting (GBM), AdaBoost, and thelike.

In some embodiments, the computing device may be further configured toreceive, via the communication interface, from the one or moredatabases, execution details and logs corresponding to the executeddatabase request. The computing device may analyze the execution detailsand logs based on one or more of Pareto analysis, causal analysis, andfailure mode and effect analysis and, based on the analysis theexecution details and logs, may update the machine learning algorithmsused to generate the legitimacy score.

In some embodiments, the computing device may be further configured todecode the database request into a plurality of objects and performchecks of each of the plurality of objects based on the predetermineddefinitions, wherein the predetermined definitions may include one ormore of access definitions, role definitions, and optimization levels.

In some embodiments, the computing device may be further configured totransmit, via the communication interface, to a database administratorcomputing device, an alert regarding the database request responsive todetermining that the legitimacy score is below the predeterminedthreshold.

In some instances, the one or more databases are one of a relationaldatabase and a NoSQL database.

These features, along with many others, are discussed in greater detailbelow.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of aspects described herein and theadvantages thereof may be acquired by referring to the followingdescription in consideration of the accompanying drawings, in which likereference numbers indicate like features, and wherein:

FIGS. 1A and 1B depict an illustrative computing environment forperforming automated request assessments in accordance with one or moreexample embodiments;

FIGS. 2A, 2B, 2C, 2D, 2E, and 2F depict an illustrative event sequencefor performing automated database request assessments in accordance withone or more example embodiments;

FIG. 3 depicts an illustrative method for performing automated databaserequest assessments in accordance with one or more example embodiments;and

FIG. 4 illustrates a network environment and computing systems that maybe used to implement one or more aspects of the disclosure.

DETAILED DESCRIPTION

In the following description of the various embodiments, reference ismade to the accompanying drawings, which form a part hereof, and inwhich is shown by way of illustration various embodiments in whichaspects described herein may be practiced. It is to be understood thatother embodiments may be utilized and structural and functionalmodifications may be made without departing from the scope of thedescribed aspects and embodiments. Aspects described herein are capableof other embodiments and of being practiced or being carried out invarious ways. Also, it is to be understood that the phraseology andterminology used herein are for the purpose of description and shouldnot be regarded as limiting. Rather, the phrases and terms used hereinare to be given their broadest interpretation and meaning. The use of“including” and “comprising” and variations thereof is meant toencompass the items listed thereafter and equivalents thereof as well asadditional items and equivalents thereof. The use of the terms“mounted,” “connected,” “coupled,” “positioned,” “engaged” and similarterms, is meant to include both direct and indirect mounting,connecting, coupling, positioning and engaging.

FIGS. 1A and 1B depict an illustrative computing environment for anintelligent database control system configured to perform automatedrequest assessments in accordance with one or more example embodiments.

Referring to FIG. 1A, computing environment 100 may include one or morecomputing devices and/or other computer systems. For example, computingenvironment 100 may include one or more user computing devices110A-110N, intelligent database control computing platform 120, one ormore databases 130A-130N, and database administrator computing device140. Each of the one or more user computing devices 110A-110N,intelligent database control computing platform 120, each of the one ormore databases 130A-130N, and database administrator computing device140 may be configured to communicate with each other, as well as withother computing devices, for example, through network 150. In someinstances, intelligent database control computing platform 120, each ofthe one or more databases 130A-130N, and database administratorcomputing device 140 may be configured to communicate with each otherthrough a local and/or internal network. Such a local and/or internalnetwork may be configured to interface with network 150 and usercomputing devices 110A-110N. Furthermore, each component of thecomputing environment 100 may include a computing device (or system)having some or all of the structural components of intelligent datacontrol computing device 401 described below in regard to FIG. 4.

Each of the user computing devices 110A-110N may be configured tointeract with the one or more databases 130A-130N by way of intelligentdatabase control computing platform 120. In particular, each of the usercomputing devices 110A-110N may be configured to receive and transmitinformation corresponding to database requests, which may be associatedwith local, remote, and/or distributed database transactions. Thedatabase requests provided by the one or more user computing devices110A-110N may include one or more of a user identification (e.g., userID) and password, email, an indication of one or more databases fromdatabases 130A-130N to be involved in the request, schema detailsassociated with the user, format of the database query, and the like. Insome instances, the user computing devices 110A-110N may requestperformance of the database transaction through a database requestprovided by way of an application configured to interface withintelligent database control computing platform 120. Additionally and/oralternatively, the database request may be sent by way of a webapplication (e.g., web graphical user interface built on shell scriptand/or power shell script) associated with intelligent database controlcomputing platform 120.

Intelligent database control computing platform 120 may include aplurality of computing devices and associated computing hardware andsoftware that may host various applications configured to receive,transmit, and/or store data, control and/or direct actions of otherdevices and/or computer systems (e.g., user computing device 110A-110N,databases 130A-130N, and database administrator computing device 140),and/or perform other functions, as discussed in greater detail below. Insome arrangements, intelligent database control computing platform 120may include and/or be part of enterprise information technologyinfrastructure and may host a plurality of enterprise applications,enterprise databases, and/or other enterprise resources. Suchapplications may, for instance, be executed on one or more computingdevices included in intelligent database control computing platform 120using distributed computing technology and/or the like. In someinstances, intelligent database control computing platform 120 mayinclude a relatively large number of servers that may support operationsof a particular enterprise or organization, such as a financialinstitution. In addition, and as discussed in greater detail below,various computing devices included in intelligent database controlcomputing platform 120 may be configured to interface with usercomputing devices 110A-110N, databases 130A-130N, and/or databaseadministrator computing device 140. Through interfacing, intelligentdatabase control computing platform 120 may receive, analyze, andexecute database requests, store result set data related to databaserequests, and/or package result set data for transmission.

Each of the databases 130A-130N may correspond to a relational databaseand/or a NoSQL database. In some instances, each of the databases130A-130N may be of a homogenous database type (e.g., relationaldatabase) and a homogenous variety, or of a heterogeneous database typeand a heterogeneous variety.

The databases 130A-130N may be configured to store data associated withone or more enterprises or organizations, respond to execution commandsfrom intelligent database control computing platform 120 correspondingto database requests provided by users associated with user computingdevices 110A-110N, and generate result data sets, based on the executioncommand provided by intelligent database control computing platform 120,in a manner indicated by the database requests provided by usersassociated with user computing devices 110A-110N.

Database administrator computing device 140 may be configured tocommunicate with, and support the operations of, one or more of the usercomputing devices 110A-110N, intelligent database control computingplatform 120, and databases 130A-130N. In particular, databaseadministrator computing device 140 may be able to receive informationfrom intelligent database control computing platform 120 and/ordatabases 130A-130N related to database requests. Additionally, databaseadministrator computing device 140 may be able to transmit informationcorresponding to predetermined definitions, which will be described indetail below, that may be used by intelligent database control computingplatform 120 and/or databases 130A-130N to analyze and execute databaserequests provided by users associated with user computing devices110A-110N.

As will be described in further detail below in regard to FIG. 4, in oneor more arrangements, the one or more user computing devices 110A-110N,intelligent database control computing platform 120, one or moredatabases 130A-130N, and database administrator computing device 140 maybe any type of computing device capable of receiving a user interface,receiving input via the user interface, and communicating the receivedinput to one or more other computing devices. For example, the one ormore user computing devices 110A-110N, intelligent database controlcomputing platform 120, one or more databases 130A-130N, and databaseadministrator computing device 140 may, in some instances, be and/orinclude server computers, desktop computers, laptop computers, tabletcomputers, smart phones, or the like that may include one or moreprocessors, memories, communication interfaces, storage devices, and/orother components. As noted above, and as illustrated in greater detailbelow, any and/or all of the one or more user computing devices110A-110N, intelligent database control computing platform 120, one ormore databases 130A-130N, and database administrator computing device140 may, in some instances, be special-purpose computing devicesconfigured to perform specific functions.

As stated above, computing environment 100 also may include one or morenetworks, which may interconnect one or more of the one or more usercomputing devices 110A-110N, intelligent database control computingplatform 120, one or more databases 130A-130N, and databaseadministrator computing device 140. For example, computing environment100 may include network 150. Network 150 may include one or moresub-networks (e.g., local area networks (LANs), wide area networks(WANs), or the like).

Referring to FIG. 1B, intelligent database control computing platform120 may include processor(s) 121, communication interface(s) 122, andmemory 123. Communication interface(s) 122 may be a network interfaceconfigured to support communication between intelligent database controlcomputing platform 120 and one or more networks (e.g., network 150).Memory 123 may include one or more program modules having instructionsthat, when executed by processor(s) 121, cause intelligent databasecontrol computing platform 120 to perform the automated database requestassessments, as well as other functions described herein. For example,memory 123 may have, store, and/or include an intelligent analyzermodule 123 a, autonomous executor module 123 b, result set handlermodule 123 c, result set packager module 123 d, execution analyzermodule 123 e, machine learning module 123 f, and control database 123 g.

Intelligent analyzer module 123 a may have instructions that facilitatemany of the automated request assessment processes described herein. Forinstance, the intelligent analyzer module 123 a may decode the datarequests provided by the one or more user computing devices 110A-110Ninto a plurality of objects. Additionally, the intelligent analyzermodule 123 a may gather resource information from each of the databasesfrom databases 130A-130N specified in the database request. The resourceinformation may include an expected completion time to produce a resultset for the request, processing consumption levels, and the detail plansfor the request. Further, the intelligent analyzer module 123 a maycheck each of the plurality of objects of the decoded data request withthe predetermined definitions including permission definitions (e.g.,authentication and authorization details associated with intelligentdatabase control system users), security role definitions (e.g.,sensitive data access details and constraints associated with a user),and optimization level definitions (e.g., database utilizationinformation) to ensure compliance and determine a legitimacy score forthe database request.

The intelligent analyzer module 123 a may compare the legitimacy scoreagainst a predetermined threshold. If the legitimacy score is below thepredetermined threshold, the intelligent analyzer module 123 a may beconfigured to provide an alert to database administrator computingdevice 140. If the legitimacy score is above the predeterminedthreshold, the intelligent analyzer module 123 a may provide thedatabase request to autonomous executor module 123 b.

The autonomous executor module 123 b may have instructions that directand/or cause the intelligent database control computing platform 120 toformat the database request in relation to one or more of the resourceinformation, the legitimacy score, and the predefined definitions. Inparticular, the autonomous executor module 123 b may use one or more ofthe resource information, legitimacy score, and predetermineddefinitions to format the database request to produce the appropriatedatabase clauses corresponding to the database request and theparticular databases associated with the database request. Additionally,the autonomous executor module 123 b may provide the formatted databaserequest to database administrator computing device 140 and may commandeach of the databases from the one or more databases 130A-130Nstipulated by the database request to execute the database request.

In some instances, the autonomous executor module 123 b may have furtherinstructions that direct and/or cause the intelligent database controlcomputing platform 120 to format the database request in relation to oneor more of the resource information, legitimacy score, and predetermineddefinitions based on machine learning module 123 f and the machinelearning algorithms included therein. As such, the autonomous executormodule 123 b may utilize the machine learning module 123 f to format thedatabase request, by way of machine learning algorithms, based onhistorical execution pattern details stored in control database 123 g toensure the request meets necessary criteria (e.g., abidance withpredetermined definitions, resource information, and the like) to beexecuted.

The result set handler module 123 c may have instructions that directand/or cause the intelligent database control computing platform 120 toreceive the result set and execution details and logs associated withthe database request from each of the databases that executed thedatabase request and format the received result set in the mannerstipulated by the database request. Further, the result set handlermodule 123 c may be configured to transmit the execution details andlogs of the database request to the database administrator computingdevice 140 and store the execution details and logs in the controldatabase 123 g.

The result set packager module 123 d may have instructions that directand/or cause the intelligent database control computing platform 120 topackage the result set and execution details into the format specifiedby the database request and transmit the packaged result set andexecution details to the user computing device from user computingdevices 110A-110N corresponding to the database request. In someinstances, the result set packager module 123 d may cause the result setand execution details to be displayed on an interface of the usercomputing device 110A-110N of the user associated with the requestcorresponding to a user login/session.

Execution analyzer module 123 e may have instructions that direct and/orcause the intelligent database control computing platform 120 to analyzethe execution details and result set through one or more of Paretoanalysis, causal analysis, and failure mode and effect analysis (FMEA).Additionally, the execution analyzer module 123 e may be configured totransmit the analysis results to the database administrator computingdevice 140 and store the analysis results in control database 123 g.

Machine learning module 123 f may have instructions that direct and/orcause the intelligent database control computing platform 120 to adjustthe performance of one or more of the intelligent analyzer module 123 a,autonomous executor module 123 b, result set handler module 123 c,result set packager module 123 d, and execution analyzer module 123 ebased on the database request information stored in control database 123g.

The control database 123 g may be configured to store the executiondetails and logs of the database request and the analysis resultsprovided by execution analyzer module 123 e. As stated above, and insome instances, such data may be used by machine learning module 123 fto calibrate the machine learning algorithms used by the intelligentanalyzer module 123 a, autonomous executor module 123 b, result sethandler module 123 c, result set packager module 123 d, and executionanalyzer module 123 e in performing the aspects of the aforementionedprocesses.

In some arrangements, as will be described in further detail below,intelligent database control computing platform 120 may be configured tointerface with user computing devices 110A-110N, databases 130A-130N,and database administrator computing device 140 to perform one or moreaspects of the automated database request assessments described herein.In such arrangements, intelligent analyzer module 123 a may beconfigured to receive, decode, and process database requests provided byuser computing devices 110A-110N. The autonomous executor module 123 bmay be configured to format database requests and command the one ormore databases 130A-130N to execute the database requests. The resultset handler module 123 c may be configured to format the result setprovided from the executed database request. The result set packagermodule 123 d may be configured to package the result set in the properformat and transmit the formatted result set to the user computingdevice 110A-110N associated with the database request. The executionanalyzer module 123 e may be configured to analyze execution details andlogs associated with the result set and transmit the results of theanalyzed execution details and logs to the database administratorcomputing device 140. The machine learning module 123 f may beconfigured to adjust the performance of one or more of the intelligentanalyzer module 123 a, autonomous executor module 123 b, result sethandler module 123 c, result set packager module 123 d, and executionanalyzer module 123 e based on the results of the database request. Thecontrol database 123 g may be configured to store data includingpredetermined definitions provided by database administrator computingdevice 140, user information associated with users of user computingdevices 110A-110N, historical data associated with previously performeddatabase requests, and the like.

FIGS. 2A, 2B, 2C, 2D, 2E, and 2F depict an illustrative event sequencefor performing automated database request assessments in accordance withone or more example embodiments. To address the above-mentionedtechnological shortcomings, and in accordance with an embodiment of thedisclosure, an intelligent database control system configured to performautomated request assessments may be provided.

Referring to FIG. 2A, at step 201, the database administrator computingdevice 140 may receive data corresponding to definitions includingpermission definitions (e.g., authentication and authorization detailsassociated with intelligent database control system users), securityrole definitions (e.g., sensitive data access details and constraintsassociated with a user), and optimization level definitions (e.g.,database utilization information) from a database administrator. At step202, the database administrator computing device 140 may transmit thedata corresponding to the definitions to intelligent database controlcomputing platform 120, which may receive the data corresponding to thedefinitions (e.g., predetermined definitions) at step 203. At step 204,the intelligent database control computing platform 120 may store thedata corresponding to the predetermined definitions in control database123 g. As stated above, the predetermined definitions may be utilized bythe intelligent database control computing device 120 in performing theautomated database request assessment methods described herein.

For example, referring to FIG. 2B and step 205, one or more of usercomputing devices 110A-110N may request performance of a database query.At step 206, the intelligent analyzer module 123 a of the intelligentdatabase control computing device 120 may receive the database requestfrom one or more user computing devices 110A-110N. The database requestmay include a user identification and password associated with a user,an indication of one or more databases 130A-130N to be involved in thedatabase request, schema details of the database request, format of thedatabase query, and the like. At step 207, the intelligent analyzermodule 123 a of the intelligent database control computing platform 120may decode the database request into a plurality of database objectscorresponding to the information included in the database request (e.g.,a user identification and password associated with a user, an indicationof one or more databases 130A-130N to be involved in the databaserequest, schema details of the database request, format of the databasequery, and the like).

At step 208, the intelligent analyzer module 123 a may perform a checkof the database request to ensure compliance with the predetermineddefinitions (e.g., permission definitions, security role definitions,and optimization level definitions). In doing so, intelligent analyzermodule 123 a may compare each of the plurality of database objectsgenerated in step 207 to the corresponding predetermined definitions tocheck compliance. For example, the intelligent analyzer module 123 a maycompare each of the plurality of database objects corresponding to theinformation included in the database request (e.g., a useridentification and password associated with a user, an indication of oneor more databases 130A-130N to be involved in the database request,schema details of the database request, format of the database query,and the like) with the predetermined definitions including permissiondefinitions (e.g., authentication and authorization details associatedwith intelligent database control system users), security roledefinitions (e.g., sensitive data access details and constraintsassociated with a user), and optimization level definitions (e.g.,database utilization information). As such, the intelligent analyzermodule 123 a may compare the database object associated with useridentification and password with the permission definitions and securityrole definitions to ensure compliance. Further, the intelligent analyzermodule 123 a may compare the database object associated with theindication of the one or more databases 130A-130N, the schema details,and format of the database query to be involved in the database requestwith the security role and optimization level definitions.

Referring to FIG. 2C, at step 209, the intelligent analyzer module 123 amay gather resource information from the one or more databases 130A-130Nidentified by user including expected processing time and availableprocessing capacity. In doing so, the intelligent analyzer module 123 amay request resource information from each of the databases fromdatabases 130A-130N indicated in the database request. In someinstances, responsive to requesting the resource information,intelligent analyzer module 123 a may identify that the resources of oneor more of the databases indicated in the database request are above apredetermined resource threshold and, as such, may delay the performanceof the database request until the resource information is identified asbeing below the predetermined threshold.

At step 210, based on the analysis of the database request regardingcompliance with the predetermined definitions received from databaseadministrator computing device 140 in step 202, the intelligent analyzermodule 123 a may generate a legitimacy score associated with thedatabase request which may reflect the level of compliance with thepredetermined definitions. To do so, intelligent analyzer module 123 amay use machine learning module 123 f to compare each of the pluralityof database objects of the database request, based on machine learningalgorithms, to historical execution pattern details stored in controldatabase 123 g to correlate the plurality of database objects of thedatabase request and predetermined definitions with the historicalexecution pattern details of a plurality of database objects of previousdatabase requests. As such, intelligent analyzer module 123 a may usemachine learning module 123 f to generate a legitimacy score, based onmachine learning algorithms, which corresponds to the compliance of thedatabase request with the predetermined definitions in view ofhistorical execution pattern details.

At step 211A, if the legitimacy score is below the predeterminedthreshold, the intelligent analyzer module 123 a may provide an alert todatabase administrator computing device 140 for a database administratorto further analyze the database request. The alert may include thelegitimacy score and information corresponding to the database requestand associated user. In some instances, if the legitimacy score is belowthe predetermined threshold, it may indicate that the database requestdoes not agree with the predetermined definitions and/or that theefficacy of the database request, as viewed by the relation of thedatabase request and the predetermined definitions, does not track wellwith the historical execution pattern details.

Conversely, at step 211B, if the legitimacy score is above apredetermined threshold (e.g., 70%, 80%, or the like), the intelligentanalyzer module 123 a may provide the database request to autonomousexecutor module 123 b of the intelligent database control computingplatform 120. As stated above, in some instances, the legitimacy scoremay be influenced by machine learning algorithms, which may be performedby machine learning module 123 f and may provide increased weight tocertain aspects of the predetermined definitions based on historicalexecution pattern details of previous database requests. In someinstances, if the legitimacy score is above the predetermined threshold,it may indicate that the database request agrees with the predetermineddefinitions and/or that the efficacy of the database request, as viewedby the relation of the database request and the predetermineddefinitions, tracks well with the historical execution pattern details.

Referring to FIG. 2D and step 212, in the event that the legitimacyscore is above the predetermined threshold, the autonomous executormodule 123 b may format the database request based on the predetermineddefinitions to ensure that data returned from the request abides by thepermission definitions, security role definitions, and optimizationlevel definitions. In some instances, the formatting performed by theautonomous executor module 123 b may be influenced by historicalexecution pattern details collected from the execution analyzer module123 e, which will be described in further detail below, based on thedegree of matching the user query with the historical execution pattern.In some instances, the autonomous executor module 123 b may leveragemachine learning module 123 f to use machine learning algorithms toformat the database request. For example, the autonomous executor module123 b may leverage machine learning module 123 f to use machine learningalgorithms to format the database request based on the predetermineddefinitions and historical execution pattern details corresponding topreviously executed database requests. As such, the formatting of thedatabase requests may become improved over time.

At step 213, the autonomous executor module 123 b may command orinstruct (e.g., transmit a signal to) the one or more databases130A-130N associated with the formatted database request to perform theoperations specified in the database request. At step 214, in responseto receiving the execution command from the autonomous executor module123 b, the one or more databases 130A-130N associated with the formatteddatabase request may perform the operations specified in the databaserequest to generate a result set. At step 215, each of the one or moredatabases 130A-130N which executed the database request may transmit thegenerated result set to the intelligent database control computingplatform 120, which may receive the result set at step 216.

Referring FIG. 2E, at step 217, the result set handler module 123 c ofthe intelligent database control computing platform 120 may format theresults of the database request based on criteria specified in thedatabase request and the predetermined definitions. In particular, theresults of the database request may be formatted by the result sethandler module 123 c to ensure compliance with the permissiondefinitions and security role definitions as related to the informationcomprised within the database request.

Further, at step 218, the result set handler module 123 c may transmitthe details and logs associated with the database request to thedatabase administrator computing device 140 and store the details andlogs in the control database 123 g at step 219 for analysis by executionanalyzer module 123 e, which will be described below. The result sethandler module 123 c may provide the formatted results of the userdatabase request to the result set packager module 123 d. At step 220,the result set packager module 123 d may package the result set and theexecution details of the database request in the format specified by theinput provided by the user associated with the database request.

Referring to FIG. 2F, the result set packager module 123 d may transmitthe packaged result set and execution details to the user. In someexamples, the packaged result set may be transmitted to the user in theform of email. Additionally and/or alternatively, the packaged resultset and execution details may be displayed on an interface of the usercomputing device 110A-110N of the user associated with the requestcorresponding to a user login/session. At step 222, the user computingdevice 110A-110N of the user associated with the request may receive theresult set and execution details associated with the database request.

At step 223, the execution analyzer module 123 d may analyze the set ofexecution details and logs via one or more of Pareto analysis, causalanalysis, and failure mode and effect analysis (e.g., FMEA). In someinstances, at step 224, the execution analyzer module 123 d may transmitthe results of the analyzed execution details and logs to the databaseadministrator computing device 140 for future events prediction andcapability prediction. Additionally and/or alternatively, at step 225and based on the results of the analyzed execution details and logs, theexecution analyzer module 123 d may be configured to revise, update,and/or calibrate the machine learning module 123 f described herein.

FIG. 3 illustrates one example method for performing automated databaserequest assessments in accordance with one or more example embodiments.Referring to FIG. 3, at step 305, a computing device having at least oneprocessor, communication interface, input mechanism, and memory, mayreceive, via the communication interface, from a user computing device,a database request. At step 310, the computing device may generate alegitimacy score associated with the database request based on one ormore predetermined definitions. At step 315, if the legitimacy score isabove a predetermined threshold, the computing device may format thedatabase request based on one or more of the predetermined definitionsand the legitimacy score. At step 320, the computing device command maycommand, via the communication interface, one or more databases toexecute the database request. At step 325, the computing device mayformat a result set generated from the executed database request. Atstep 330, the computing device may transmit, via the communicationinterface, to the user computing device, the formatted result set.

FIG. 4 illustrates a block diagram of an intelligent data controlcomputing device 401 in a system that may be used according to one ormore illustrative embodiments of the disclosure. The intelligent datacontrol computing device 401 may have a processor 403 for controllingoverall operation of the intelligent data control computing device 401and its associated components, including RAM 405, ROM 407, input/outputmodule 409, and memory unit 415. The intelligent data control computingdevice 401, along with one or more additional devices (e.g., terminals441, 451) may correspond to any of multiple systems or devices, such asdispatch management systems, configured as described herein forperforming automated request assessments.

Input/Output (I/O) module 409 may include a microphone, keypad, touchscreen, and/or stylus through which a user of the intelligent datacontrol computing device 401 may provide input, and may also include oneor more of a speaker for providing audio input/output and a videodisplay device for providing textual, audiovisual and/or graphicaloutput. Software may be stored within memory unit 415 and/or otherstorage to provide instructions to processor 403 for enablingintelligent data control computing device 401 to perform variousfunctions. For example, memory unit 415 may store software used by theintelligent data control computing device 401, such as an operatingsystem 417, application programs 419, and an associated internaldatabase 421. The memory unit 415 includes one or more of volatileand/or non-volatile computer memory to store computer-executableinstructions, data, and/or other information. Processor 403 and itsassociated components may allow the intelligent data control computingdevice 401 to execute a series of computer-readable instructions toperform the one or more of the processes or functions described herein.

The intelligent data control computing device 401 may operate in anetworked environment 400 supporting connections to one or more remotecomputers, such as terminals/devices 441 and 451. Intelligent datacontrol computing device 401, and related terminals/devices 441 and 451,may include devices installed in vehicles and/or homes, mobile devicesthat may travel within vehicles and/or may be situated in homes, ordevices outside of vehicles and/or homes that are configured to performaspects of the processes described herein. Thus, the intelligent datacontrol computing device 401 and terminals/devices 441 and 451 may eachinclude personal computers (e.g., laptop, desktop, or tablet computers),servers (e.g., web servers, database servers), vehicle-based devices(e.g., on-board vehicle computers, short-range vehicle communicationsystems, sensors, and telematics devices), or mobile communicationdevices (e.g., mobile phones, portable computing devices, and the like),and may include some or all of the elements described above with respectto the intelligent data control computing device 401. The networkconnections depicted in FIG. 4 include a local area network (LAN) 425and a wide area network (WAN) 429, and a wireless telecommunicationsnetwork 433, but may also include other networks. When used in a LANnetworking environment, the intelligent data control computing device401 may be connected to the LAN 425 through a network interface oradapter 423. When used in a WAN networking environment, the intelligentdata control computing device 401 may include a modem 427 or other meansfor establishing communications over the WAN 429, such as network 431(e.g., the Internet). When used in a wireless telecommunications network433, the intelligent data control computing device 401 may include oneor more transceivers, digital signal processors, and additionalcircuitry and software for communicating with wireless computing devices441 (e.g., mobile phones, short-range vehicle communication systems,vehicle sensing and telematics devices) via one or more network devices435 (e.g., base transceiver stations) in the wireless network 433.

It will be appreciated that the network connections shown areillustrative and other means of establishing a communications linkbetween the computers may be used. The existence of any of variousnetwork protocols such as TCP/IP, Ethernet, FTP, HTTP and the like, andof various wireless communication technologies such as GSM, CDMA, Wi-Fi,and WiMAX, is presumed, and the various computing devices and componentsdescribed herein may be configured to communicate using any of thesenetwork protocols or technologies.

Additionally, one or more application programs 419 used by the computingdevice 401 may include computer executable instructions for receivingdata and performing other related functions as described herein.

The various aspects described herein may be embodied as a method, acomputer system, or a computer program product. Accordingly, thoseaspects may take the form of an entirely hardware embodiment, anentirely software embodiment or an embodiment combining software andhardware aspects. Furthermore, such aspects may take the form of acomputer program product stored by one or more computer-readable storagemedia having computer-readable program code, or instructions, embodiedin or on the storage media. Any suitable computer readable storage mediamay be utilized, including hard disks, CD-ROMs, optical storage devices,magnetic storage devices, and/or any combination thereof. In addition,various signals representing data or events as described herein may betransferred between a source and a destination in the form ofelectromagnetic waves traveling through signal-conducting media such asmetal wires, optical fibers, and/or wireless transmission media (e.g.,air and/or space).

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described above.Rather, the specific features and acts described above are disclosed asexample forms of implementing the claims.

What is claimed is:
 1. An intelligent database control computingplatform, comprising: at least one processor; a communication interfacecommunicatively coupled to the at least one processor; and memorystoring computer-readable instructions that, when executed by the atleast one processor, cause the intelligent database control computingplatform to: receive, via the communication interface, from a usercomputing device, a database request; generate a legitimacy scoreassociated with the database request based on one or more predetermineddefinitions; determine whether the generated legitimacy score is above apredetermined threshold; responsive to determining that the legitimacyscore is above the predetermined threshold, format the database requestbased on one or more of the predetermined definitions and the legitimacyscore; command, via the communication interface, one or more databasesto execute the database request; format a result set generated from theexecuted database request; and transmit, via the communicationinterface, to the user computing device, the formatted result set. 2.The intelligent database control computing platform of claim 1, whereinthe legitimacy score is generated using machine learning algorithms. 3.The intelligent database control computing platform of claim 2, whereinthe computer-readable instructions, when executed by the at least oneprocessor, further cause the intelligent database control computingplatform to: receive, via the communication interface, from the one ormore databases, execution details and logs corresponding to the executeddatabase request; analyze the execution details and logs based on one ormore of Pareto analysis, causal analysis, and failure mode and effectanalysis; and update the machine learning algorithms used to generatethe legitimacy score based on the analysis of the execution details andlogs.
 4. The intelligent database control computing platform of claim 3,wherein the computer-readable instructions, when executed by the atleast one processor, further cause the intelligent database controlcomputing platform to: decode the database request into a plurality ofobjects; and perform checks of each of the plurality of objects based onthe predetermined definitions.
 5. The intelligent database controlcomputing platform of claim 1, wherein the predetermined definitionsinclude one or more of access definitions, role definitions, andoptimization levels.
 6. The intelligent database control computingplatform of claim 1, wherein the computer-readable instructions, whenexecuted by the at least one processor, further cause the intelligentdatabase control computing platform to: responsive to determining thatthe legitimacy score is below the predetermined threshold, transmit, viathe communication interface, to a database administrator computingdevice, an alert regarding the database request.
 7. The intelligentdatabase control computing platform of claim 1, wherein the one or moredatabases are one of a relational database and a NoSQL database.
 8. Amethod, comprising: at a computing platform comprising at least oneprocessor, memory, and a communication interface: receiving, via thecommunication interface, from a user computing device, a databaserequest; generating a legitimacy score associated with the databaserequest based on one or more predetermined definitions; determinewhether the generated legitimacy score is above a predeterminedthreshold; responsive to determining that the legitimacy score is abovethe predetermined threshold, formatting the database request based onone or more of the predetermined definitions and the legitimacy score;commanding, via the communication interface, one or more databases toexecute the database request; formatting result set generated from theexecuted database request; and transmitting, via the communicationinterface, to the user computing device, the formatted result set. 9.The method of claim 8, wherein the legitimacy score is generated usingmachine learning algorithms.
 10. The method of claim 9, furthercomprising: receive, via the communication interface, from the one ormore databases, execution details and logs corresponding to the executeddatabase request; analyze the execution details and logs based on one ormore of Pareto analysis, causal analysis, and failure mode and effectanalysis; and update the machine learning algorithms used to generatethe legitimacy score based on the analysis of the execution details andlogs.
 11. The method of claim 10, further comprising: decoding thedatabase request into a plurality of objects; and performing checks ofeach of the plurality of objects based on the predetermined definitions.12. The method of claim 8, wherein the predetermined definitions includeone or more of access definitions, role definitions, and optimizationlevels.
 13. The method of claim 8, further comprising: responsive todetermining that the legitimacy score is below the predeterminedthreshold, transmitting, via the communication interface, to a databaseadministrator computing device, an alert regarding the database request.14. The method of claim 13, wherein the one or more databases are one ofa relational database and a NoSQL.
 15. One or more non-transitorycomputer-readable media storing instructions that, when executed by acomputing device comprising at least one processor, memory, and acommunication interface, cause the computing platform to: receive, viathe communication interface, from a user computing device, a databaserequest; generate a legitimacy score associated with the databaserequest based on one or more predetermined definitions; determinewhether the generated legitimacy score is above a predeterminedthreshold; responsive to determining that the legitimacy score is abovethe predetermined threshold, format the database request based on one ormore of the predetermined definitions and the legitimacy score; command,via the communication interface, one or more databases to execute thedatabase request; format result set generated from the executed databaserequest; and transmit, via the communication interface, to the usercomputing device, the formatted result set.
 16. The one or morenon-transitory computer-readable media of claim 15, wherein thelegitimacy score is generated using machine learning algorithms.
 17. Theone or more non-transitory computer-readable media of claim 16, storingadditional instructions that, when executed by the computing device,cause the computing device to: receive, via the communication interface,from the one or more databases, execution details and logs correspondingto the executed database request; analyze the execution details and logsbased on one or more of Parato analysis, casual analysis, and failuremode and effect analysis; and update the machine learning algorithmsused to generate the legitimacy score based on the analysis of theexecution details and logs.
 18. The one or more non-transitorycomputer-readable media of claim 17, storing additional instructionsthat, when executed by the computing device, cause the computing deviceto: decode the database request into a plurality of objects; and performchecks of each of the plurality of objects based on the predetermineddefinitions.
 19. The one or more non-transitory computer-readable mediaof claim 15, wherein the predetermined definitions include one or moreof access definitions, role definitions, and optimization levels andwherein the one or more databases are one of a relational database and aNoSQL database.
 20. The one or more non-transitory computer-readablemedia of claim 19, storing additional instructions that, when executedby the computing device, cause the computing device to: responsive todetermining that the legitimacy score is below the predeterminedthreshold, transmit, via the communication interface, to a databaseadministrator computing device, an alert regarding the database request.